Title :
Power transformer equivalent circuit identification by artificial neural network using frequency response analysis
Author :
Zambrano, G.M.V. ; Ferreira, A.C. ; Calôba, L.P.
Author_Institution :
COPPE/UFRJ, Rio de Janeiro
Abstract :
This paper presents a methodology to estimate the parameters of a transformer model that simulates its operation over a wide frequency range. An artificial neural networks (ANN) is used to estimate the transfer function of the transformer winding from data obtained from frequency response measurements
Keywords :
equivalent circuits; frequency measurement; frequency response; neural nets; parameter estimation; power engineering computing; power transformers; transfer functions; transformer windings; ANN; artificial neural network; equivalent circuit identification; frequency response analysis; frequency response measurements; parameters estimation; power transformer; transfer function; transformer winding; Artificial neural networks; Circuit simulation; Equivalent circuits; Frequency estimation; Frequency measurement; Frequency response; Parameter estimation; Power transformers; Transfer functions; Windings; Genetic Algorithms; Transformer; frequency response; neural networks;
Conference_Titel :
Power Engineering Society General Meeting, 2006. IEEE
Conference_Location :
Montreal, Que.
Print_ISBN :
1-4244-0493-2
DOI :
10.1109/PES.2006.1708931
